Teresa Vogl

ORCID • E-Mail

2022 Vogl, T., M. Maahn, S. Kneifel, W. Schimmel, D. Moisseev, and H. Kalesse, 2022: Using artificial neural networks to predict riming from Doppler cloud radar observations. Atmos. Meas. Tech., https://doi.org/10.5194/amt-2021-137
2021

• Trömel, S. and Simmer, C. and Blahak, U. and Blanke, A. and Doktorowski, S. and Ewald, F. and Frech, M. and Gergely, M. and Hagen, M. and Janjic, T. and Kalesse-Los, H. and Kneifel, S. and Knote, C. and Mendrok, J. and Moser, M. and Köcher, G. and Mühlbauer, K. and Myagkov, A. and Pejcic, V. and Seifert, P. and Shrestha, P. and Teisseire, A. and von Terzi, L. and Tetoni, E. and Vogl, T. and Voigt, C. and Zeng, Y. and Zinner, T. and Quaas, J.: Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes, Atmos. Chem. Phys., https://doi.org/10.5194/acp-21-17291-2021, 2021.